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Overview of Natural language processing


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Title: Overview of Natural language processing

Natural language processing playing (NLP)
  • What is NLP?
  • Natural language processing (NLP) is an integral
    part of AI, Computer Science, and Linguistics.
    NLP is all about making computers/machines as
    intelligent as human beings in the understanding
    of natural-communication language like text,
    speech, and so on. It comprises 2 major
    functionalities. they are Human to machine
    translation and Machine to Human translation.
  • here are 3 different levels of linguistic
    analysis done before performing NLP
  • Syntax - What part of the given text is
    grammatically right.
  • Semantics - What is the meaning of the given
  • Pragmatics - What is the purpose of the text?

NLP Techniques that must know    1. Named Entity
Recognition (NER) 2. Tokenization 3. Stemming 4.
Bag of Words 5. Natural language generation 6.
Sentiment Analysis  7. Sentence Segmentation
  • Named Entity Recognition (NER) This technique is
    quite possibly the most important and famous in
    Semantic analysis, Semantics is something
    conveyed by the text. Here the algorithm takes a
    phrase or passage as input and identifies all the
    nouns or names present in that information.
  • Tokenization Tokenization is basically splitting
    the whole content into a list of tokens, lists
    can be anything such as words, sentences,
    characters, numbers, punctuation, etc. It is an
    integral part of the Information Retrieval (IR)
    system, it generates tokens respectively that are
    used in the indexing/ranking process.
  • Stemming Stemming is a process of reducing words
    from the base root word means it cuts all
    suffixes from the main root word. For example,
    Running becomes Run.
  • Bag of words The bag of words technique is used
    to pre-process text and to extract all the
    features from a text document to use in Machine
    Learning modelling. 
  • Natural language generation This technique
    converts raw data into natural plain text for a
    better understanding. 
  • Sentiment analysis the main task of this
    technique is to understand the emotion or feeling
    of the written text. It is also known as Emotion
  • Sentence segments this technique is used to
    identify the text and divide that text into
    meaningful sentences.

  • Key Application Areas of NLP
  • We are using NLP every day in Machine learning
    and AI applications. Below are few key
    application areas of NLP. Have a look.
  • Automatic Text Summarizer Given the input text,
    the task is to compose an outline of text
    disposing of unessential points.
  • Sentiment-based Text Analysis It is done on the
    given text to anticipate the subject of the
    content, for example, regardless of whether the
    content conveys judgment, opinion, or audits, and
    so on.
  • Text Classification It is performed to order
    various journals, news stories according to their
    domain. Multi-report grouping is additionally
    conceivable. A popular illustration of text
    grouping is spam recognition in messages and
    emails. Based on the style of writing in the
    journal, its attribute can be utilized to
    distinguish its writer's name.
  • Data Extraction Data extraction is something
    which proposes email programs add occasions to
    the schedule consequently.

  • Till now you have learned about NLP, its
    techniques and application areas. Now its time to
    implement NLP in your Application. If youre
    looking out to integrate NLP in your application
    connect with V2Soft A leading AI service
    provider helps you to design and develop AI
    applications with NLP integration.
  • Related Topics
  • Artificial intelligent robots
  • Top 6 Industries that uses Chatbot Application
  • Testing AI Systems 

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